Massive Data Classification via Unconstrained Support Vector Machines
نویسندگان
چکیده
منابع مشابه
Massive Data Classification via Unconstrained Support Vector Machines
A highly accurate algorithm, based on support vector machines formulated as linear programs [13, 1], is proposed here as a completely unconstrained minimization problem [15]. Combined with a chunking procedure [2] this approach, which requires nothing more complex than a linear equation solver, leads to a simple and accurate method for classifying million-point datasets. Because a 1-norm suppor...
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Abst ract A linear support vector machine formulation is used to generate a fast, nitely-terminating linear-programming algorithm for discriminating between two massive sets in n-dimensional space, where the number of points can be orders of magnitude larger than n. The algorithm creates a succession of suuciently small linear programs that separate chunks of the data at a time. The key idea is...
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Classifying data is a common task in machine learning. In machine learning, statistical classification is the problem of identifying the sub-population to which new observations belong on the basis of a training set of data containing observations whose sub-population is known. Therefore these classifications will show a variable behavior which can be studied by statistics. In machine learning,...
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ژورنال
عنوان ژورنال: Journal of Optimization Theory and Applications
سال: 2006
ISSN: 0022-3239,1573-2878
DOI: 10.1007/s10957-006-9157-x